Publications

BACKGROUND

Abnormal acid gastro-oesophageal reflux is common in patients with idiopathic pulmonary fibrosis (IPF) and is considered a risk factor for development of IPF. Retrospective studies have shown improved outcomes in patients given anti-acid treatment. The aim of this study was to investigate the association between anti-acid treatment and disease progression in IPF.

METHODS

In an analysis of data from three randomised controlled trials, we identified patients with IPF assigned to receive placebo. Case report forms had been designed to prospectively obtain data about diagnosis and treatment of abnormal acid gastro-oesophageal reflux in each trial. The primary outcome was estimated change in forced vital capacity (FVC) at 30 weeks (mean follow-up) in patients who were and were not using a proton-pump inhibitor or histamine-receptor-2 (H2) blocker.

FINDINGS

Of the 242 patients randomly assigned to the placebo groups of the three trials, 124 (51%) were taking a proton-pump inhibitor or H2 blocker at enrolment. After adjustment for sex, baseline FVC as a percentage of predicted, and baseline diffusing capacity of the lung for carbon monoxide as a percentage of predicted, patients taking anti-acid treatment at baseline had a smaller decrease in FVC at 30 weeks (-0·06 L, 95% CI -0·11 to -0·01) than did those not taking anti-acid treatment (-0·12 L, -0·17 to -0·08; difference 0·07 L, 95% CI 0-0·14; p=0·05).

INTERPRETATION

Anti-acid treatment could be beneficial in patients with IPF, and abnormal acid gastro-oesophageal reflux seems to contribute to disease progression. Controlled clinical trials of anti-acid treatments are now needed.

FUNDING

National Institutes of Health.

Gene expression data, in conjunction with information on genetic variants, have enabled studies to identify expression quantitative trait loci (eQTLs) or polymorphic locations in the genome that are associated with expression levels. Moreover, recent technological developments and cost decreases have further enabled studies to collect expression data in multiple tissues. One advantage of multiple tissue datasets is that studies can combine results from different tissues to identify eQTLs more accurately than examining each tissue separately. The idea of aggregating results of multiple tissues is closely related to the idea of meta-analysis which aggregates results of multiple genome-wide association studies to improve the power to detect associations. In principle, meta-analysis methods can be used to combine results from multiple tissues. However, eQTLs may have effects in only a single tissue, in all tissues, or in a subset of tissues with possibly different effect sizes. This heterogeneity in terms of effects across multiple tissues presents a key challenge to detect eQTLs. In this paper, we develop a framework that leverages two popular meta-analysis methods that address effect size heterogeneity to detect eQTLs across multiple tissues. We show by using simulations and multiple tissue data from mouse that our approach detects many eQTLs undetected by traditional eQTL methods. Additionally, our method provides an interpretation framework that accurately predicts whether an eQTL has an effect in a particular tissue.